Suppose there are 256 sampling points along each spoke, a fully-sampled radial k-space needs \(256*\pi /2 \approx 402\)  spokes according to the Nyquist sampling requirement [1]}. In this work we aim to achieve 4 times undersampling and thus set the number of spokes of the input to be 100 and the output has 300 spokes to supplement the acquired ones. Because the prediction accuracy decreases with the length of the sequence, we propose to train 3 independent Transformer networks, each predicting 100 consecutive spokes of different angles.
